Parametric Regression Through Genetic Programming

نویسندگان

  • Edwin Roger Banks
  • James C. Hayes
  • Edwin Nunez
چکیده

Parametric regression in genetic programming can substantially speed up the search for solutions. In this paper parametric regression is applied to a minimum-time-to-target problem. The solution is equivalent to the classical brachistochrone. Two formulations were tried: a parametric regression and the classical symbolic regression formulation. The parametric approach was superior under a wide variety of conditions. We speculate the parametric approach is more generally applicable to other problems and suggest areas for more research.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Comparison of Gene Expression Programming (GEP) and Parametric and Non-parametric Regression Methods in the Prediction of the Mean Daily Discharge of Karun River (A case Study: Mollasani Hydrometric Station)

Nowadays, the prediction of river discharge is one of the important issues in hydrology and water resources; the results of daily river discharge pattern could be used in the management of water resources and hydraulic structures and flood prediction. In this research, Gene Expression Programming (GEP), parametric Linear Regression (LR), parametric Nonlinear Regression (NLR) and non-parametric ...

متن کامل

Predictive Ability of Statistical Genomic Prediction Methods When Underlying Genetic Architecture of Trait Is Purely Additive

A simulation study was conducted to address the issue of how purely additive (simple) genetic architecture might impact on the efficacy of parametric and non-parametric genomic prediction methods. For this purpose, we simulated a trait with narrow sense heritability h2= 0.3, with only additive genetic effects for 300 loci in order to compare the predictive ability of 14 more practically used ge...

متن کامل

Using Multiobjective Genetic Programming to Infer Logistic Polynomial Regression Models

In designing non-linear classifiers, there are important trade-offs to be made between predictive accuracy and model comprehensibility or complexity. We introduce the use of Genetic Programming to generate logistic polynomial models, a relatively comprehensible non-linear parametric model; describe an efficient twostage algorithm consisting of GP structure design and Quasi-Newton coefficient se...

متن کامل

Dynamic environments can speed up evolution with Genetic Programming Technical Report UCD-CSI-2011-03

We present a study of dynamic environments with genetic programming to ascertain if a dynamic environment can speed up evolution when compared to an equivalent static environment. We present an analysis of the types of dynamic variation which can occur with a variable-length representation such as adopted in genetic programming identifying modular varying, structural varying and incremental var...

متن کامل

Separation Surfaces through Genetic Programming

The aim of this paper is to describe a study for the obtaining, symbolically, of the separation surfaces between clusters of a labelled database. A separation surface is an equation with the form φ (x)=0, where φ is a function of R→ R. The calculation of function φ is begun by the development of the parametric regression by means of the use of the Genetic Programming. The symbolic regression co...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2004